Capture and modeling of non-linear heterogeneous soft tissue

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Metallophones such as glockenspiels produce sounds in response to contact. Building these instruments is a complicated process, limiting their shapes to well-understood designs such as bars. We automatically optimize the shape of arbitrary 2D and 3D objects through deformation and perforation to produce sounds when struck which match user-supplied frequency and amplitude spectra. This optimization requires navigating a complex energy landscape, for which we develop Latin Complement Sampling to both speed up finding minima and provide probabilistic bounds on landscape exploration. Our method produces instruments which perform similarly to those that have been professionallymanufactured, while also expanding the scope of shape and sound that can be realized, e.g., single object chords. Furthermore, we can optimize sound spectra to create overtones and to dampen specific frequencies. Thus our technique allows even novices to design metallophones with unique sound and appearance.

Section: Contact Sound Simulationmentioning

confidence: 99%

Computational design of metallophone contact sounds

Bharaj¹,

Levin

Tompkin³

et al. 2015

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“…Bickel et al [2009] measured force-displacement to compute a spatially varying set of Young's moduli, interpolated in strain space. Our work also involves learning new constitutive models for finite element methods with several key differences.…”

Section: Related Workmentioning

confidence: 99%

Data-driven finite elements for geometry and material design

Chen¹,

Levin

Sueda

et al. 2015

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Figure 1: Examples produced by our data-driven finite element method. Left: A bar with heterogeneous material arrangement is simulated 15x faster than its high-resolution counterpart. Left-Center: Our fast coarsening algorithm dramatically accelerates designing this shoe sole (up to 43x). Right-Center: A comparison to 3D printed results. Right: We repair a flimsy bridge by adding a supporting arch (8.1x) speed-up. We show a High-Res Simulation for comparison. AbstractCrafting the behavior of a deformable object is difficult-whether it is a biomechanically accurate character model or a new multimaterial 3D printable design. Getting it right requires constant iteration, performed either manually or driven by an automated system. Unfortunately, previous algorithms for accelerating three-dimensional finite element analysis of elastic objects suffer from expensive precomputation stages that rely on a priori knowledge of the object's geometry and material composition. In this paper we introduce Data-Driven Finite Elements as a solution to this problem. Given a material palette, our method constructs a metamaterial library which is reusable for subsequent simulations, regardless of object geometry and/or material composition. At runtime, we perform fast coarsening of a simulation mesh using a simple table lookup to select the appropriate metamaterial model for the coarsened elements. When the object's material distribution or geometry changes, we do not need to update the metamaterial library-we simply need to update the metamaterial assignments to the coarsened elements. An important advantage of our approach is that it is applicable to non-linear material models. This is important for designing objects that undergo finite deformation (such as those produced by multimaterial 3D printing). Our method yields speed gains of up to two orders of magnitude while maintaining good accuracy. We demonstrate the effectiveness of the method on both virtual and 3D printed examples in order to show its utility as a tool for deformable object design.

“…Elastic Behavior: In the spirit of Bickel et al [2009] we have implemented an algorithm to compute material distribution based on a desired force-displacement response. The input to this algorithm is a mesh, a simulation configuration, and a desired shape.…”

Section: Goal-based Causticsmentioning

confidence: 99%

“…The plot shows the error (in meters) as a function of iteration number of our branch and bound based tuner. The blue line shows the smallest error seen so far while the red line the tuner's progress exploring material subtrees [Bickel et al 2009]. …”

Section: Goal-based Causticsmentioning

confidence: 99%

Spec2Fab

Chen¹,

Levin²,

Didyk³

et al. 2013

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Figure 1: 3D-printed objects with various effects designed using our reducer-tuner model. Our generalized approach to fabrication enables an easy and intuitive design of objects with different material properties. On the left: a miniature of Earth with a prescribed deformation behavior. On the right: an optimized surface producing a caustic image under proper illumination as well as casting a shadow of a previously designed shape. Insets visualize an input to our system. AbstractMulti-material 3D printing allows objects to be composed of complex, heterogeneous arrangements of materials. It is often more natural to define a functional goal than to define the material composition of an object. Translating these functional requirements to fabricable 3D prints is still an open research problem. Recently, several specific instances of this problem have been explored (e.g., appearance or elastic deformation), but they exist as isolated, monolithic algorithms. In this paper, we propose an abstraction mechanism that simplifies the design, development, implementation, and reuse of these algorithms. Our solution relies on two new data structures: a reducer tree that efficiently parameterizes the space of material assignments and a tuner network that describes the optimization process used to compute material arrangement. We provide an application programming interface for specifying the desired object and for defining parameters for the reducer tree and tuner network. We illustrate the utility of our framework by implementing several fabrication algorithms as well as demonstrating the manufactured results.